Let me break this down into clear categories because the space is quite fragmented, and different platforms solve different problems:
What it is: Serverless GPU compute specifically designed for Python ML workloads Strengths:
- Lightning fast: Provisions A100s in seconds
- Python-native: Write functions, deploy instantly
- Auto-scaling: 0 to thousands of GPUs automatically
- $30/month free credits
Example:
import modal
app = modal.App()
@app.function(gpu="A100", timeout=3600)
def train_model():
# Your training code here
return results
# Deploy instantlyvs HF Jobs: Modal is more flexible for custom Python workloads, HF Jobs better for HF ecosystem integration
What it is: Pay-per-second serverless GPU with Docker containers Strengths:
- Cheapest pricing: Often 50-70% less than big cloud providers
- Docker-based: Any containerized workload
- Global edge locations: Lower latency
vs HF Jobs: More cost-effective, but requires Docker knowledge
What it is: Now focused on secure infrastructure for AI-generated code execution Interesting: They pivoted from dev environments to AI code sandboxing - worth watching
What it is: Full MLOps platform with Jupyter notebooks + GPU compute Strengths:
- Complete MLOps: Training, deployment, monitoring in one platform
- Multi-cloud: Run on-prem, AWS, GCP, etc.
- Team collaboration: Shared notebooks, experiments
vs HF Jobs: More comprehensive but also more complex to set up
What it is: Cloud development environments with persistent storage Strengths:
- VS Code in browser with GPU access
- Persistent environments: Don't lose your work
- PyTorch Lightning integration
What it is: Peer-to-peer GPU marketplace Strengths:
- Extremely cheap: RTX 4090 for $0.24/hour, H100 for $2.13/hour
- Massive selection: Hundreds of GPU types available
Gotchas:
- Nodes can disappear: Mid-training crashes are common
- No SLA: Consumer hardware, not enterprise reliability
What it is: Optimized GPU capacity reseller Strengths:
- Best price/reliability ratio: A100 40GB for $0.57/hour
- One-click VS Code: Easy development setup
- No waitlist: Instant access
What it is: Developer-focused GPU cloud Strengths:
- Developer experience: Great CLI, simple pricing
- Reliable hardware: Enterprise-grade GPUs
- Pre-configured environments: ML frameworks ready to go
vs HF Jobs: Better for long-running training, HF Jobs better for quick experiments
What it is: Kubernetes-native GPU cloud for enterprises Strengths:
- Massive scale: Deploy thousands of GPUs
- Enterprise features: SLAs, dedicated support
- Kubernetes-native: Full orchestration
What it is: Sustainable GPU cloud using stranded energy Strengths:
- Cost effective: A100 80GB for $1.45/hour
- Green computing: Uses waste natural gas
- Volume discounts: 10-30% off for commitments
The Hidden Players
- Ultra-cheap: Competing directly with Vast.ai on price
- 99.99% uptime SLA: More reliable than peer-to-peer
- Low margins: They reinvest everything into the platform
- Polished experience: Enterprise-grade platform
- European focus: Good for EU data residency
- Stable infrastructure: Less experimental than others
| Provider | A100 40GB/hour | A100 80GB/hour | H100/hour |
|---|---|---|---|
| HF Jobs | ~$2.75 | ~$4.50 | ~$8.25 |
| Thunder Compute | $0.57 | $0.95 | $2.40 |
| Vast.ai | $1.50-2.50 | $2.13-3.00 | $3.50-5.00 |
| Lambda Labs | $1.29 | $2.06 | $4.60 |
| Modal | $1.60 | $2.40 | $4.10 |
| AWS/GCP/Azure | $3.67-4.10 | $6.40-7.65 | $27.20+ |
- Integrated ML workflows: Using HF models, datasets, spaces
- Simplicity over cost: You value ease of use over cheapest price
- UV scripts: Perfect integration with self-contained scripts
- Quick experiments: Fast setup for one-off tasks
Modal: Python-heavy workloads, need auto-scaling, want serverless Thunder Compute: Best price/reliability for standard ML training Vast.ai: Maximum cost savings, can handle occasional failures Lambda Labs: Long training runs, need reliable support Paperspace: Full MLOps pipeline, team collaboration
Smart teams are using multiple platforms:
# Development and experimentation
hf jobs uv run --flavor cpu-basic quick_test.py
# Serious training (cost-optimized)
thunder_compute_cli run --gpu a100-40gb long_training.py
# Production inference (reliable)
modal deploy inference_service.py
# Team collaboration
# Use Paperspace Gradient for shared notebooksThe Holy Grail: A platform that combines:
- HF Jobs' simplicity
- Thunder Compute's pricing
- Modal's auto-scaling
- Lambda's reliability
- Vast.ai's hardware variety
Current Reality: You have to choose your tradeoffs.
For HF Ecosystem Users: Start with HF Jobs, supplement with cheaper alternatives for heavy compute
For Cost-Conscious Developers: Thunder Compute or Vast.ai for training, Modal for inference
For Enterprise Teams: CoreWeave or Crusoe with long-term contracts
For Python ML: Modal is hard to beat for developer experience
For Everything Else: Lambda Labs strikes the best balance
The GPU cloud space is evolving rapidly - expect consolidation and new players as AI demand grows. But right now, no single platform is perfect for all use cases.⏎